TO laser automatic production system and method based on machine vision

Abstract

The invention provides a TO laser automatic production system and method based on machine vision, and relates to the technical field of semiconductor lasers, the system comprises a laser automation device, an industrial camera, an image background plate and a machine vision module; the laser automation equipment comprises a control motor, a starting switch, a pressing device, a TO laser, a tool table and a positioning clamp. The industrial camera is installed on the side face of the tool table and used for collecting images projected to the image background plate by the TO laser in real time. The image background plate is installed right in front of the positioning clamp. The machine vision is used for processing the image acquired by the industrial camera, performing image processing on the image and outputting a control signal; the control motor comprises a relay, and the relay is used for controlling the downward pressing device to achieve downward pressing termination operation and reset operation according to the control signal. Manual intervention can be reduced, the production efficiency and the consistency of the product quality are improved, the cost is reduced, and the production flexibility is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of semiconductor lasers, and in particular to a TO laser automated production system and method based on machine vision. Background Art

[0002] The TO laser automated production system based on machine vision is a system that uses machine vision technology and automation equipment to precisely manufacture TO packaged lasers. The system can ensure that the quality of the laser meets the preset standards and improve the consistency and stability of the production line during the efficient and precise production process.

[0003] With the increasing demand for smart home appliances and high-precision lasers, traditional manual or semi-automated production methods cannot meet the requirements of high efficiency and quality consistency. The TO laser automated production system based on machine vision can accurately control the laser production process through real-time image detection, effectively improve production efficiency, reduce human errors, and ensure product consistency and stability, thereby greatly improving the automation level of the production line, reducing production costs, and meeting the market demand for high-quality and high-efficiency production.

[0004] However, the existing laser press-assembly process mostly relies on manual work, which makes it difficult to guarantee product quality and has low production efficiency. A small number of lasers rely on mechanical automation and are pressed using fixed position parameters. However, due to factors such as material tolerances, TO laser consistency differences, assembly environment, and different product requirements, all lasers are pressed using fixed position parameters, which makes it impossible to keep the quality of each product stable and maintain high consistency. Summary of the Invention

[0005] In view of the above deficiencies in the prior art, the purpose of the embodiments of the present invention is to provide an automated production method for TO lasers based on machine vision, which can solve the technical problem that the pressing process of existing lasers mostly relies on manual work, the manual production method is difficult to ensure product quality, and the production efficiency is low; a small part relies on mechanical automation and adopts a pressing method with set fixed position parameters for pressing, but due to the influence of factors such as material tolerance, TO laser consistency differences, assembly environment, and different product requirements, all lasers are pressed using a method with set fixed position parameters, which makes it impossible to keep the quality of each product in a stable state and maintain a high consistency.

[0006] A first aspect of an embodiment of the present invention provides a TO laser automated production system based on machine vision, comprising: laser automation equipment, an industrial camera, an image background plate, and a machine vision module; The laser automation equipment includes: a control motor, a start switch, a pressing device, a TO laser, a tooling table and a positioning fixture; The industrial camera is installed on the side of the workbench, and is used to collect the image projected by the TO laser onto the image background plate in real time; The image background plate is installed right in front of the positioning fixture; The machine vision module is used to process the image captured by the industrial camera, perform image processing on the image, and output a control signal; The control motor includes a relay, and the relay is used to control the pressing device to implement a pressing termination operation and a reset operation according to the control signal.

[0007] A second aspect of the embodiments of the present invention provides a method for automated production of TO lasers based on machine vision, which is applied to the automated production system of TO lasers based on machine vision in the first aspect. The method includes: S1: Start the laser automation equipment; S2: using the industrial camera to capture the image projected by the TO laser onto the background plate; S3: Preprocess the image to generate a mask; S4: performing morphological processing on the mask to obtain a morphologically processed image; S5: extracting multiple contours of the morphologically processed image; S6: Extracting all valid contours whose contour areas are within a preset area range from each of the contours, and calculating the minimum circumscribed rectangle of each of the valid contours.

[0008] S7: Screening the minimum bounding rectangle to determine the target area; S8: Determine whether the target area meets the width and height determination principle and the circular area inclusion determination principle; if so, mark it as a good product; otherwise, mark it as a defective product; S9: sending the judgment result as a control signal to the control motor; S10: Controlling the pressing device to perform a pressing termination operation and a reset operation according to the control signal.

[0009] According to a third aspect of an embodiment of the present invention, a readable storage medium is provided, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the automated TO laser production method based on machine vision as described in the second aspect are implemented.

[0010] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least: In an embodiment of the present invention, through the combination of laser automation equipment, industrial cameras, image background plates and machine vision modules, the production process can be detected and adjusted in real time to ensure that the pressing of each laser reaches the optimal state, improve the stability of product quality, significantly reduce the intervention of manual operations, and improve the work efficiency of the production line. Through precise image processing and real-time quality detection, the size, position, brightness and other parameters of each TO laser can be efficiently detected to ensure that each product meets the quality standards and that each laser produced has consistent quality. By using a machine vision module in the automated pressing process to make real-time judgments on the various states of the pressing process, the pressing of each laser can be achieved in the optimal state. The machine vision module can reduce manual intervention through automated image analysis and judgment, thereby reducing quality fluctuations caused by human errors, can adapt to the production needs of different batches of products, and improve the versatility and adaptability of the production line. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The accompanying drawings are only for the purpose of illustrating specific embodiments and are not to be considered as limiting the present invention. Throughout the drawings, the same reference symbols represent the same components. Obviously, the drawings described below are only some embodiments of the present invention. It is clear that those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0012] Figure 1 1 is a schematic structural diagram of a TO laser automated production system based on machine vision provided by an embodiment of the present invention; Figure 2 This is a schematic structural diagram of a laser automation device provided by an embodiment of the present invention; Figure 3 This is a flow chart of a TO laser automated production method based on machine vision provided by an embodiment of the present invention.

[0013] Figure numerals: 1. Laser automation equipment; 2. Industrial camera; 3. Image background plate; 11. Control motor; 12. Pressing device; 13. Positioning fixture; 14. Start switch.

[0014] As shown in the figure, in order to clearly implement the structure of the embodiment of the present invention, specific structures and devices are marked in the figure, but this is only for illustrative purposes and is not intended to limit the present invention to the specific structure, device and environment. According to specific needs, ordinary technicians in this field can adjust or modify these devices and environments. DETAILED DESCRIPTION

[0015] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are part of the embodiments of the present invention, rather than all of the embodiments. It should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work should fall within the scope of protection of the present invention.

[0016] The machine vision-based TO laser automated production method provided by the embodiment of the present invention is described in detail below with reference to the accompanying drawings through specific embodiments and their application scenarios.

[0017] Reference Manual Figure 1 , shows a structural schematic diagram of a TO laser automated production system based on machine vision provided by an embodiment of the present invention.

[0018] Reference Manual Figure 2 , shows a structural schematic diagram of a laser automation device provided by an embodiment of the present invention.

[0019] The embodiment of the present invention provides a TO laser automated production system based on machine vision, comprising: laser automation equipment, an industrial camera, an image background plate, and a machine vision module; The laser automation equipment includes: a control motor, a start switch, a pressing device, a TO laser, a tooling table and a positioning fixture; The industrial camera is installed on the side of the workbench, and is used to collect the image projected by the TO laser onto the image background plate in real time; Specifically, industrial cameras capture laser images in real time and, through high-precision image data analysis, can promptly detect deviations in the production process, ensuring that each laser meets quality standards and improving product consistency.

[0020] The image background plate is installed right in front of the positioning fixture.

[0021] The image background plate is at a certain distance from the laser and serves as a target for the laser to project the image.

[0022] Specifically, the image background plate provides a clear reference background to ensure that the images captured by the industrial camera are accurate, providing a reliable data source for subsequent image processing of the machine vision module, thereby improving the accuracy of image analysis.

[0023] The machine vision module is used to process the image captured by the industrial camera, perform image processing on the image, and output a control signal; The control motor includes a relay, and the relay is used to control the pressing device to implement a pressing termination operation and a reset operation according to the control signal.

[0024] In the present invention, the component to be produced is first placed in a positioning fixture, the TO laser is placed in a pressing device, and the switch is started. During the pressing process, the laser state is judged in real time through machine vision, and a control signal is obtained. The control motor resets the pressing equipment according to the control signal.

[0025] In a possible implementation manner, the laser is disposed inside the pressing device.

[0026] Specifically, placing the TO laser inside the pressing device can ensure that the laser maintains a stable position during the press-fitting process, avoiding position deviation due to external interference, thereby improving the accuracy and consistency of press-fitting.

[0027] Reference Manual Figure 3 , which shows a flow chart of a TO laser automated production method based on machine vision provided by an embodiment of the present invention.

[0028] An embodiment of the present invention provides a method for automated production of TO lasers based on machine vision, comprising: S1: Start the laser automation equipment.

[0029] S2: Use an industrial camera to capture the image of the TO laser projected onto the background plate.

[0030] It should be noted that the industrial camera can capture the image projected by the laser onto the background board with high precision, ensuring the accuracy and clarity of the image data.

[0031] S3: Preprocess the image and generate a mask.

[0032] It should be noted that through image preprocessing, the amount of calculation can be significantly reduced, and the areas of interest in the image can be extracted. The generated mask can clearly distinguish the target area from the background, simplifying the subsequent image analysis process.

[0033] In a possible implementation, the preprocessing includes: resizing and color space conversion.

[0034] Among them, color space conversion is the process of converting an image from one color model to another color model. The purpose of color space conversion is to make the image more suitable for certain specific tasks in different spaces.

[0035] In a possible implementation, S3 specifically includes: S301: Crop the image and determine the cropped image: ; in, W Indicates the width of the image, H Indicates the height of the image, ( x 1, y 1) represents the coordinates of the upper left corner of the cropped image, ( x 2, y 2) represents the coordinates of the upper right corner of the cropped image, I crop Represents the pixel value of the cropped image; S302: Calculate the average grayscale of the local area of ​​the cropped image: ; in, μ roi represents the average grayscale of the local area, N represents the total number of pixels in the local domain, Representing coordinates The pixel gray value of the point, ( x 0, y 0) represents the coordinates of the lower left corner of the cropped image, that is, the coordinate origin, w Indicates the width of the local area, h Indicates the height of the local area.

[0036] The local area average grayscale refers to the average grayscale value of all pixels in a specific area of ​​the image, which reflects the overall brightness information of the area and is usually used to describe the brightness distribution of an image in a certain local area.

[0037] It should be noted that by calculating the grayscale average value of the local area of ​​the image, the influence of local noise can be effectively eliminated, and more stable image features can be obtained, which minimizes the impact of factors such as image lighting changes and background interference on subsequent analysis, thereby improving the accuracy of image processing.

[0038] S303: Determine the dynamic grayscale threshold according to the average grayscale of the local area: ; in, T ( x , y )express( x , y ) is the gray value after binarization, I ( x , y ) means cropping the image in ( x , y ), T base represents the basic threshold; Among them, the dynamic grayscale threshold is an adaptive threshold calculated based on the grayscale characteristics of the local area of ​​the image. It is used to distinguish the area of ​​interest in the image (such as the highlight area) from the background. It can be automatically adjusted according to the specific content of the image (such as the brightness of the local area), making the image processing more accurate under different lighting conditions.

[0039] S304: Extracting the highlighted white area in the cropped image according to the dynamic grayscale threshold and generating a mask.

[0040] Specifically, through dynamic grayscale threshold calculation, the threshold can be automatically adjusted according to the brightness changes of different image areas, avoiding the errors that may be caused by fixed thresholds, so that the details in the image can be extracted more accurately. At the same time, it can also adapt to the influence of external conditions such as lighting changes, thereby improving the accuracy of image segmentation and feature extraction.

[0041] Specifically, by calculating the dynamic grayscale threshold, the highlight area and background in the image can be accurately distinguished, thereby generating a clear mask that can automatically identify and extract the target area, reduce background interference, and improve the accuracy of image segmentation.

[0042] S4: Perform morphological processing on the mask to obtain a morphologically processed image.

[0043] Among them, morphological processing is an operation based on image shape, usually used for binary images. It improves the structure of the image by using specific structural elements (such as rectangles, circles, ellipses, etc.) to perform operations such as dilation, erosion, opening, and closing operations on the image. Morphological processing is often used for tasks such as removing noise, filling gaps, highlighting or removing specific shapes.

[0044] It should be noted that morphological operations can effectively remove noise and small holes in the mask, fill gaps and smooth boundaries, making the target area clearer and more coherent, enhancing the features in the image, eliminating unnecessary background interference, and improving image quality.

[0045] In a possible implementation, S4 is specifically: Through the ellipse kernel closing operation, the mask is morphologically processed to obtain the morphologically processed image: ; in, represents the morphologically processed image, Represents a mask, represents the expansion operation, represents the erosion operation, and B represents the elliptical structure element.

[0046] Closing is an operation in morphological processing that involves dilating an image and then eroding it to eliminate small holes, fill small gaps, or connect broken objects. The elliptical kernel is a structuring element used in closing operations, typically an elliptical matrix. Dilation and erosion operations operate on this elliptical structure to alter the foreground and background areas.

[0047] It should be noted that the closing operation can effectively fill the small holes and gaps in the mask, connect the scattered foreground areas, enhance the connectivity of the target area, and reduce the influence of noise. It not only clearly separates the target area, but also removes small objects or noise in the image, thereby improving image quality.

[0048] S5: Extract multiple contours of the morphologically processed image.

[0049] It should be noted that contour extraction can accurately identify each independent object or area in the image, and further analyze and judge the target in the image. Contour extraction can clarify the boundaries of different objects in the image and effectively remove irrelevant background information, making subsequent size calculation, target screening and quality judgment more accurate, and enhancing the structural information of the image.

[0050] S6: Extract all valid contours whose contour areas are within a preset area range from each contour, and calculate the minimum circumscribed rectangle of each valid contour.

[0051] Specifically, by screening valid contours that meet the preset area range, it is possible to accurately focus on the target object, eliminate interference and noise, and reduce misjudgment. The calculation of the minimum enclosing rectangle provides a standardized bounding box for each valid contour, making subsequent shape analysis, position calibration, and quality judgment more accurate.

[0052] It should be noted that those skilled in the art can set the size of the preset area range according to actual needs, and the present invention is not limited thereto.

[0053] In the present invention, invalid contours with too small or too large areas are filtered out, and all valid contours whose contour areas meet the preset area range are extracted.

[0054] S7: Screen the minimum bounding rectangle to determine the target area.

[0055] It should be noted that by screening the minimum enclosing rectangle that meets specific conditions (such as aspect ratio, brightness, etc.), the target area that meets the quality standards can be accurately identified, the contours that do not meet the requirements can be excluded, the focus can be placed on the real target area, and the risk of incorrect judgment can be reduced.

[0056] In a possible implementation, S7 specifically includes: S701: Calculate the aspect ratio and brightness ratio of the minimum bounding rectangle; S702: Screening the minimum bounding rectangle according to the aspect ratio and the brightness ratio to determine the target area.

[0057] In a possible implementation, the aspect ratio is specifically: ; Among them, aspect_ratio represents the aspect ratio, height represents the height of the minimum bounding rectangle, and width represents the width of the minimum bounding rectangle. ε Represents a positive infinitesimal variable approaching zero to avoid the denominator being zero; The brightness ratio is specifically: ; Among them, brightness_ratio represents the brightness ratio, A rect Represents the area of ​​the minimum enclosing rectangle, and rect represents the minimum enclosing rectangular area.

[0058] S8: Determine whether the target area meets the width and height determination principle and the circular area inclusion determination principle; if so, mark it as a good product; otherwise, mark it as a defective product.

[0059] It should be noted that through width and height judgment and circular area containment judgment, it is possible to accurately determine whether the target area meets the set size and shape requirements. The width and height judgment ensures that the size of the target area is within a reasonable range, while the circular area containment judgment helps to determine whether the shape of the target area is close to the ideal circle. These standardized judgment steps reduce human errors, improve the accuracy of quality inspection, and ensure that each product can meet the predetermined specifications, thereby improving production efficiency and product consistency.

[0060] In a possible implementation, whether the target area meets the width and height determination principle is specifically as follows: ; in,( w min , w max ) indicates the allowed width range, ( h min , h max ) indicates the permitted height range, Represents the width of the rectangle, Represents the height of the rectangle, Represents the intersection symbol.

[0061] The specific principles for determining the inclusion of a circular area are as follows: ; in,( c 1, c 2) represents the coordinates of the center point of the circle, ( c x , c y ) represents the center point of the effective rectangle, r 0 represents the circle radius.

[0062] S9: Sending the judgment result as a control signal to the control motor; S10: Controlling the pressing device to perform pressing termination operation and reset operation according to the control signal.

[0063] It should be noted that by automatically receiving control signals, the action of the pressing device can be accurately controlled to ensure that the laser pressing process stops at the appropriate time, avoiding the risk of over-pressing or incomplete pressing. The pressing termination operation effectively improves the stability and accuracy of the production process, and the reset operation ensures that the equipment can be quickly ready for the next production, reducing the idle time of the equipment.

[0064] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least: In an embodiment of the present invention, through the combination of laser automation equipment, industrial cameras, image background plates and machine vision modules, the production process can be detected and adjusted in real time to ensure that the pressing of each laser reaches the optimal state, improve the stability of product quality, significantly reduce the intervention of manual operations, and improve the work efficiency of the production line. Through precise image processing and real-time quality detection, the size, position, brightness and other parameters of each TO laser can be efficiently detected to ensure that each product meets the quality standards and that each laser produced has consistent quality. By using a machine vision module in the automated pressing process to make real-time judgments on the various states of the pressing process, the pressing of each laser can be achieved in the optimal state. The machine vision module can reduce manual intervention through automated image analysis and judgment, thereby reducing quality fluctuations caused by human errors, can adapt to the production needs of different batches of products, and improve the versatility and adaptability of the production line.

[0065] The above embodiments can be implemented in whole or in part via software, hardware (e.g., circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present invention are fully or partially performed. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired means (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0066] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0067] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0068] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0069] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of the device or unit, which can be electrical, mechanical or other forms.

[0070] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0071] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0072] If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0073] An embodiment of the present invention provides a readable storage medium including: a program or instruction is stored on the readable storage medium, and when the program or instruction is executed by the processor, the steps of the above-mentioned machine vision-based TO laser automated production method are implemented, and the same technical effect can be achieved. To avoid repetition, the present invention will not be repeated.

[0074] Finally, it should be noted that the above embodiments are merely illustrative of the technical solutions of the embodiments of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they may still modify the technical solutions described in the aforementioned embodiments, or replace some of the technical features therein with equivalents; and such modifications or replacements do not deviate from the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention. Any changes or replacements that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be covered by the scope of protection of the present invention.

Claims

1. A TO laser automated production system based on machine vision, characterized in that: include: Laser automation equipment, industrial cameras, image background plates, and machine vision modules; The laser automation equipment includes: a control motor, a start switch, a pressing device, a TO laser, a tooling table and a positioning fixture; The industrial camera is installed on the side of the workbench, and is used to collect the image projected by the TO laser onto the image background plate in real time; The image background plate is installed right in front of the positioning fixture; The machine vision module is used to process the image captured by the industrial camera, perform image processing on the image, and output a control signal; The control motor includes a relay, and the relay is used to control the pressing device to implement a pressing termination operation and a reset operation according to the control signal.

2. The TO laser automated production system based on machine vision according to claim 1, characterized in that: The laser is arranged inside the pressing device.

3. A TO laser automated production method based on machine vision, characterized in that: The machine vision-based TO laser automated production system according to any one of claims 1 to 2 comprises: S1: Start the laser automation equipment; S2: using the industrial camera to capture the image projected by the TO laser onto the background plate; S3: Preprocess the image to generate a mask; S4: performing morphological processing on the mask to obtain a morphologically processed image; S5: extracting multiple contours of the morphologically processed image; S6: extracting all valid contours whose contour areas are within a preset area range from each of the contours, and calculating the minimum circumscribed rectangle of each of the valid contours; S7: Screening the minimum bounding rectangle to determine the target area; S8: Determine whether the target area meets the width and height determination principle and the circular area inclusion determination principle; if so, mark it as a good product; otherwise, mark it as a defective product; S9: sending the judgment result as a control signal to the control motor; S10: Controlling the pressing device to perform a pressing termination operation and a reset operation according to the control signal.

4. The TO laser automated production method based on machine vision according to claim 3, characterized in that: The preprocessing includes: size adjustment and color space conversion.

5. The TO laser automated production method based on machine vision according to claim 3, characterized in that: The S3 specifically includes: S301: Cropping the image and determining the cropped image: ； in, W Indicates the width of the image, H Indicates the height of the image, ( x 1, y 1) represents the coordinates of the upper left corner of the cropped image, ( x 2, y 2) represents the coordinates of the upper right corner of the cropped image, I crop Represents the pixel value of the cropped image; S302: Calculate the average grayscale of the local area of ​​the cropped image: ； in, μ roi represents the average grayscale of the local area, N represents the total number of pixels in the local domain, Representing coordinates The pixel gray value of the point, ( x 0, y 0) represents the coordinates of the lower left corner of the cropped image, that is, the coordinate origin, w Indicates the width of the cropped image, h Indicates the height of the local area; S303: Determine a dynamic grayscale threshold according to the average grayscale of the local area: ； in, T ( x , y )express( x , y ) is the gray value after binarization, I ( x , y ) means cropping the image in ( x , y ), T base represents the basic threshold; S304: Extracting a highlighted white area in the cropped image according to the dynamic grayscale threshold to generate a mask.

6. The TO laser automated production method based on machine vision according to claim 3, characterized in that: The S4 is specifically: The mask is morphologically processed by an ellipse kernel closing operation to obtain a morphologically processed image: ； in, represents the morphologically processed image, Represents a mask, represents the expansion operation, represents the corrosion operation, B Represents an ellipse structuring element.

7. The TO laser automated production method based on machine vision according to claim 3, characterized in that: The S7 is specifically: S701: Calculating the aspect ratio and brightness ratio of the minimum circumscribed rectangle; S702: Determine the target area according to the aspect ratio and the brightness ratio.

8. The TO laser automated production method based on machine vision according to claim 7, characterized in that: The aspect ratio is specifically: ； Among them, aspect_ratio represents the aspect ratio, height represents the height of the minimum bounding rectangle, and width represents the width of the minimum bounding rectangle. ε Represents a positive infinitesimal variable approaching zero to avoid the denominator being zero; The brightness ratio is specifically: ； Among them, brightness_ratio represents the brightness ratio, A rect Represents the area of ​​the minimum enclosing rectangle, and rect represents the minimum enclosing rectangular area.

9. The TO laser automated production method based on machine vision according to claim 3, characterized in that: Whether the target area complies with the width and height determination principles is specifically as follows: ； in,( w min , w max ) indicates the allowed width range, ( h min , h max ) indicates the permitted height range, Represents the width of the rectangle, Represents the height of the rectangle, Indicates the intersection symbol; The circular area inclusion determination principle is specifically as follows: ； in,( c 1, c 2) represents the coordinates of the center point of the circle, ( c x , c y ) represents the center point of the target area, r 0 represents the circle radius.

10. A readable storage medium, characterized in that: The readable storage medium stores a program or instruction, and when the program or instruction is executed by the processor, the steps of the TO laser automated production method based on machine vision as described in any one of claims 3 to 9 are implemented.

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